Mutations in the pH-Sensing G-protein-Coupled Receptor GPR68 Cause Amelogenesis Imperfecta

Amelogenesis is the process of dental enamel formation, leading to the deposition of the hardest tissue in the human body. This process requires the intricate regulation of ion transport and controlled changes to the developing enamel matrix pH. The means by which the enamel organ regulates pH during amelogenesis is largely unknown. We identified rare homozygous variants in GPR68 in three families with Amelogenesis Imperfecta, a genetically and phenotypically heterogeneous group of inherited conditions associated with abnormal enamel formation. Each of these homozygous variants (a large in-frame deletion, a frameshift deletion and a missense) were predicted to result in loss of function. GPR68 encodes a proton sensing G-protein-coupled receptor with sensitivity in the pH range that occurs in the developing enamel matrix during amelogenesis. Immunohistochemistry of rat mandibles confirmed localisation of GPR68 in the enamel organ at all stages of amelogenesis. Our data identify a role for GPR68 as a proton sensor that is required for proper enamel formation

Additional file 1: of Isolation and characterization of antimutagenic components of Glycyrrhiza aspera against N-methyl-N-nitrosourea

Fractionations of antimutagenic compounds from dichloromethane soluble fraction. (DOCX 3675 kb

REN-A.I.: A Video Game for AI Security Education Leveraging Episodic Memory

Education in cybersecurity is crucial in the current society, and it will be extended into the artificial intelligence (AI) area, called AI security, in the near future. Although many video games for education in cybersecurity have been designed, we have two problems for education in AI security: a helpful design of a video game for users to learn cybersecurity is still unclear, and there is no game for AI security, to the best of our knowledge. In this paper, we design a video game for education in AI security, REN-A.I., to address the above problems. In designing REN-A.I., we built some hypotheses: simulating damage caused by attacks on AI and the effectiveness of their countermeasures through a video game helps a user to improve awareness of AI security with the episodic memory of the user itself. We focus on game scenarios and game functionalities to learn AI security with episodic memory in accordance with the above hypothesis. We conducted a questionnaire survey with 48 users to evaluate REN-A.I.. As a result, we confirm that both game scenarios and game functionalities are effective for learning with episodic memory. Specifically, 74% of users consider game scenarios effective, and 81% of users consider game functionalities effective. Our survey results have revealed two suggestions for beneficial design aspects in video games for education in cybersecurity. In particular, users who read game scenarios in REN-A.I. can learn AI security by the game more effectively than the other users. Furthermore, the functionality for accuracy deterioration due to attacks in REN-A.I. is effective even for users who do not read the game scenario. REN-A.I. is publicly available (https://www-infosec.ist.osaka-u.ac.jp/software/ren-ai/REN-AI(EN).html)

Re-examination of Evidence-based Librarianship (EBL) : a content analysis of journal articles

Analyzing the intraday dataset on weather and market information with the use of the extended GJR-GARCH framework, this study explores in depth the weather effects on the asset returns and volatilities of the Korean stock and derivatives markets. Our intraday analyses contribute to the existing literature by going beyond the attempt of prior studies to capture the weather effects using the average daily observations alone. The empirical results document a modest presence of the weather effect on the returns and volatilities, though the significance of its impact is found to vary across different market conditions and indices. We also find that the return and volatility respond asymmetrically to extremely good and bad weather conditions. The intraday analyses show that the weather effect on the returns and volatilities is more statistically significant at the beginning of the working day or the lunch break, indicating the intraday weather effects on the financial market